I Stress-Tested Cube's New AI Analytics Agent

Why It Matters

As organizations increasingly rely on AI for data analysis, ensuring accuracy and reliability is critical to avoid costly misinterpretations. Cube's semantic‑layer strategy shows a viable path to mitigate hallucinations, making AI analytics safer for real‑world decision‑making. This episode is timely for anyone evaluating AI‑powered BI solutions or building trustworthy AI systems.

I Stress-Tested Cube's New AI Analytics Agent

Joe Reis · Jan 29, 2026

One of my weird side hobbies is stress‑testing AI agents. I even developed a framework to consistently test them out (to be launched publicly sometime soon).

Most AI analytics agents fail in predictable ways. They hallucinate tables and joins, infer weird semantics from schemas, and give plausible but incorrect answers. Frankly, a lot of what’s out there isn’t ready for real analytical work.

I stress‑tested Cube’s new analytics agent, and it worked very well. Cube has been the OG semantic‑layer and headless BI company for a long time, and that shows in how they approach agents.

The key difference is the semantic layer. The agent queries semantic models, not raw schemas. That means it operates inside defined guardrails instead of improvising. In one test, I asked for data that didn’t exist, and it refused rather than hallucinating an answer. You get a pat on the back, AI.

Check out my unboxing and review of Cube’s analytics agent below.

This video was sponsored by Cube. To stay on‑brand, I had full editorial control, and these are my own tests and honest opinions.